Wenqian Shen

Joint CSIT Acquisition Based on Low-Rank Matrix Completion for FDD Massive MIMO Systems

Channel state information at the transmitter (CSIT) is essential for frequency-division duplexing (FDD) massive MIMO systems, but conventional solutions involve overwhelming overhead both for downlink channel training and uplink channel feedback. In this letter, we propose a joint CSIT acquisition scheme to reduce the overhead. Particularly, unlike conventional schemes where each user individually estimates its own channel and then feed it back to the base station (BS), we propose that all scheduled users directly feed back the pilot observation to the BS, and then joint CSIT recovery can be realized at the BS. We further formulate the joint CSIT recovery problem as a low-rank matrix completion problem by utilizing the low-rank property of the massive MIMO channel matrix, which is caused by the correlation among users. Finally, we propose a hybrid low-rank matrix completion algorithm based on the singular value projection to solve this problem. Simulations demonstrate that the proposed scheme can provide accurate CSIT with lower overhead than conventional schemes.

Joint Channel Training and Feedback for FDD Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) is widely recognized as a promising technology for future 5G wireless communication systems. To achieve the theoretical performance gains in massive MIMO systems, accurate channel-state information at the transmitter (CSIT) is crucial. Due to the overwhelming pilot signaling and channel feedback overhead, however, conventional downlink channel estimation and uplink channel feedback schemes might not be suitable for frequency-division duplexing (FDD) massive MIMO systems. In addition, these two topics are usually separately considered in the literature. In this paper, we propose a joint channel training and feedback scheme for FDD massive MIMO systems. Specifically, we first exploit the temporal correlation of time-varying channels to propose a differential channel training and feedback scheme, which simultaneously reduces the overhead for downlink training and uplink feedback. We next propose a structured compressive sampling matching pursuit (S-CoSaMP) algorithm to acquire a reliable CSIT by exploiting the structured sparsity of wireless MIMO channels. Simulation results demonstrate that the proposed scheme can achieve substantial reduction in the training and feedback overhead.

A Low-Complexity Linear Precoding Scheme Based on SOR Method for Massive MIMO Systems

Conventional linear precoding schemes in massive multiple-input-multiple-output (MIMO) systems, such as regularized zero-forcing (RZF) precoding, have near-optimal performance but suffer from high computational complexity due to the required matrix inversion of large size. To solve this problem, we propose a successive overrelaxation (SOR)-based precoding scheme to approximate the matrix inversion by exploiting the asymptotically orthogonal channel property in massive MIMO systems. The proposed SOR- based precoding can reduce the complexity by about one order of magnitude, and it can also approach the classical RZF precoding with negligible performance loss. We also prove that the proposed SOR-based precoding enjoys a faster convergence rate than the recently proposed Neumann-based precoding. In addition, to guarantee the performance of SOR-based precoding, we propose a simple way to choose the optimal relaxation parameter in practical massive MIMO systems. Simulation results verify the advantages of SOR-based precoding in convergence rate and computational complexity in typical massive MIMO configurations.

On the Performance of Channel-Statistics-Based Codebook for Massive MIMO Channel Feedback

The channel feedback overhead for massive multiple-input multiple-output systems with a large number of base station (BS) antennas is very high since the number of feedback bits of traditional codebooks scales linearly with the number of BS antennas. To reduce the feedback overhead, an effective codebook based on channel statistics has been designed, where the required number of feedback bits only scales linearly with the rank of the channel correlation matrix. However, this attractive conclusion was only proved under a particular channel assumption in the literature. To provide a rigorous theoretical proof under a general channel assumption, in this paper, we quantitatively analyze the performance of the channel-statistics-based codebook. Specifically, we first introduce the rate gap between the ideal case of perfect channel state information at the transmitter and the practical case of limited channel feedback, where we find that the rate gap depends on the quantization error of the codebook. Then, we derive an upper bound of the quantization error, based on which we prove that the required number of feedback bits to ensure a constant rate gap only scales linearly with the rank of the channel correlation matrix. Finally, numerical results are provided to verify this conclusion.

Richardson Method Based Linear Precoding with Low Complexity for Massive MIMO Systems

For massive MIMO system with hundreds of antennas at the base station (BS), zero forcing (ZF) precoding can achieve the near-optimal capacity due to the asymptotically orthogonal channel, but it involves complicated matrix inversion of large size. In this paper, we propose a Richardson Method (RM) based precoding to avoid the complicated matrix inversion in an iterative way, which can reduce the complexity by one order of magnitude. We also prove that the optimal relaxation parameter to RM can be approached by a simple and quantified value to maximize the convergence rate of RM-based precoding, which only depends on the number of BS antennas and the number of users. Simulation results show that RM-based precoding can achieve the near-optimal performance of ZF precoding with only a small number of iterations.

AoD-adaptive subspace codebook for channel feedback in FDD massive MIMO systems

Channel feedback is essential for frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) systems to realize precoding and power allocation. Traditional codebooks for channel feedback, where the required number of feedback bits is proportional to the number of base station (BS) antennas, can not scale up with massive MIMO due to the large number of BS antennas. To solve this problem, in this paper, we propose an angle-of-departure (AoD) adaptive subspace codebook to reduce the codebook size and feedback overhead. Specifically, by leveraging the concept of angle coherence time, which implies that the path AoDs vary much slower than path gains, we propose an AoD-adaptive subspace codebook to quantize the channel vector in a more accurate way. We also provide performance analysis of the proposed AoD-adaptive subspace codebook, where we prove that the required number of feedback bits only scales linearly with the number of resolvable AoDs, which is much smaller than the number of BS antennas. This quantitative result is also verified by simulations.

Differential CSIT Acquisition Based on Compressive Sensing for FDD Massive MIMO Systems

To fully exploit advantages of massive MIMO, channel state information at the transmitter (CSIT) is essential to obtain the system performance gains. By far, both channel estimation and channel feedback have been proposed for FDD massive MIMO by exploiting the sparsity of CSI, but they are usually separately discussed, which may impair the CSIT acquisition performance and lead to unnecessary complex computation for users. In this paper, we propose the structured-CS based differential CSIT acquisition scheme for massive MIMO systems, where the downlink channel training and uplink channel feedback are jointly considered. Specifically, we first exploit the temporal correlation of time- varying channels to propose the differential CSIT acquisition scheme, which can reduce both the overhead for downlink training and uplink feedback. Then, we propose the structured compressive sampling matching pursuit (S-CoSaMP) algorithm to further reduce overhead by leveraging the structured sparsity of wireless MIMO channels. Moreover, the proposed differential operation and S-CoSaMP can also be used at users for better channel estimation performance if channel state information at the receiver is needed. Simulation results have demonstrated that the proposed scheme can achieve better CSIT acquisition performance than its counterparts.

Temporal correlation based sparse channel estimation for TDS-OFDM in high-speed scenarios

Accurate channel estimation is essential for time domain synchronous OFDM (TDS-OFDM), which is a key enabling technology in digital terrestrial multimedia broadcasting (DTMB) standard. However, conventional channel estimation schemes for TDS-OFDM systems suffer from the obvious performance loss in high-speed scenarios. In this paper, by exploiting the temporal correlation of wireless channels, we propose a sparse channel estimation scheme to improve the channel estimation performance for TDS-OFDM systems in high-speed scenarios. Specifically, we first propose an overlap-add method of the received time-domain training sequences (TSs) to acquire the rough channel estimation, whereby the temporal correlation of wireless channels is exploited to improve the estimation performance of time-varying channels. Then, a priori information aided matching pursuit (PIA-MP) algorithm is proposed to acquire the accurate channel estimation with low complexity, whereby the priori information from the rough channel estimation is utilized to further improve the channel estimation accuracy. Simulation results demonstrate that the proposed scheme is superior to the state-of-the-art schemes in high-speed scenarios, especially under severe multipath channels with long delay spread.

Massive MIMO channel estimation based on block iterative support detection

Massive MIMO has become a promising key technology for future 5G wireless communications to increase the channel capacity and link reliability. However, with greatly increased number of transmit antennas at the base station (BS) in massive MIMO systems, the pilot overhead for accurate acquisition of channel state information (CSI) will be prohibitively high. To address this issue, we propose a block iterative support detection (block-ISD) based algorithm for channel estimation to reduce the pilot overhead. The proposed block-ISD algorithm fully exploits the block sparsity inherent in the block-sparse equivalent channel impulse response (CIR) generated by considering the spatial correlations of MIMO channels. Furthermore, unlike conventional greedy compressive sensing (CS) algorithms that rely on prior knowledge of the channel sparsity level, block-ISD relaxes this demanding requirement and is thus more practically appealing. Simulation results demonstrate that block-ISD yields better normalized mean square error (NMSE) performance than classical CS algorithms, and achieve a reduction of 87.5% pilot overhead than conventional channel estimation techniques.

Simultaneous Multi-Channel Reconstruction for TDS-OFDM Systems

Time domain synchronous orthogonal frequency division multiplexing (TDS-OFDM) has higher spectral efficiency than standard cyclic prefix OFDM (CP- OFDM), which is achieved by using a known pseudorandom noise (PN) sequence to replace the classical CP. However, due to the interference between the PN sequence and the data block, the performance of TDS-OFDM degrades severely over fast fading channels. To solve this problem, based on the distributed compressive sensing (DCS) theory, we propose an efficient way to realize simultaneous multi-channel reconstruction, which is achieved by using the inter-block-interference (IBI)-free region to reconstruct the high-dimensional sparse multipath channel. Specifically, we propose to utilize the temporal correlation of wireless channels as well as the channel property that path gains change much faster than path delays to simultaneously reconstruct multiple sparse channels. Then, we propose the parameterized channel estimation method based on simultaneous compressive sampling matching pursuit (S-CoSaMP) algorithm to achieve better channel estimation performance in fast time-varying channels. Simulation results demonstrate that the proposed scheme can achieve improved performance than conventional solutions.